Constrained networks include, for example, Low power and Lossy Networks (LLNs), such as sensor networks. These constrained networks have a myriad of applications, such as Smart Grid, Smart Cities, home and building automation, etc. Various challenges are presented with LLNs, such as lossy links, low bandwidth, battery operation, low memory and/or processing capability, etc. Large-scale IP smart object networks pose a number of technical challenges. For instance, the degree of density of such networks (such as Smart Grid networks with a large number of sensors and actuators, smart cities, or advanced metering infrastructure (“AMI”) networks) may be extremely high. For example, it is not rare for each node to see several hundreds of neighbors. This architecture is particularly problematic for LLNs, where constrained links can wreak havoc on data transmission.
Applying quality of service (QoS) techniques is thus generally desired to maintain data transmission reliability and control delays in LLNs. However, since the devices themselves are also constrained, the complexity of QoS in such networks can be problematic. That is, a primary challenge lies in the overall complexity of QoS architectures in LLNs. For instance, in conventional networks, policies must be specified for packet coloring and congestion avoidance algorithms must be configured on nodes, in addition to queuing disciplines. These algorithms all generally require a deep knowledge of the traffic pattern, link-layer characteristics, node resources, etc. and comprise a number of parameters to configure on each individual device to effectively provide adequate network-wide QoS.